class_docstr: Partial dependence plots as defined in Friedman's paper on
  "Greedy function approximation: a gradient boosting machine".
  
  Friedman, Jerome H. "Greedy function approximation: a gradient boosting machine."
  Annals of statistics (2001): 1189-1232.
class_docstr_tok: ["Partial","dependence","plots","as","defined","in","Friedman","'s","paper","on","''","Greedy","function","approximation",":","a","gradient","boosting","machine","''",".","Friedman",",","Jerome","H.","``","Greedy","function","approximation",":","a","gradient","boosting","machine",".","''","Annals","of","statistics","(","2001",")",":","1189-1232","."]
docstr: Provides approximate global explanation for blackbox model.
  
  Args:
      name: User-defined explanation name.
  
  Returns:
      An explanation object, visualizes dependence plots.
docstr_tok: ["Provides","approximate","global","explanation","for","blackbox","model",".","Args",":","name",":","User-defined","explanation","name",".","Returns",":","An","explanation","object",",","visualizes","dependence","plots","."]
code: def explain_global(self, name=None):
      """Provides approximate global explanation for blackbox model.
  
      Args:
          name: User-defined explanation name.
  
      Returns:
          An explanation object, visualizes dependence plots.
      """
      if name is None:
          name = gen_name_from_class(self)
  
      data_dicts = []
      feature_list = []
      density_list = []
      for col_idx, feature in enumerate(self.feature_names_in_):
          pdp = self.pdps_[col_idx]
          feature_dict = {
              "feature_values": pdp["values"],
              "scores": pdp["scores"],
              "upper_bounds": pdp["upper_bounds"],
              "lower_bounds": pdp["lower_bounds"],
          }
          feature_list.append(feature_dict)
          density_list.append(pdp["density"])
          data_dicts.append(pdp)
  
      internal_obj = {
          "overall": None,
          "specific": data_dicts,
          "mli": [
              {
                  "explanation_type": "pdp",
                  "value": {"feature_list": feature_list},
              },
              {
                  "explanation_type": "density",
                  "value": {"density": density_list},
              },
          ],
      }
  
      selector = gen_global_selector(
          len(self.feature_names_in_),
          self.feature_names_in_,
          self.feature_types_in_,
          self.unique_val_counts_,
          None,
      )
  
      return PDPExplanation(
          "global",
          internal_obj,
          feature_names=self.feature_names_in_,
          feature_types=self.feature_types_in_,
          name=name,
          selector=selector,
      )
  
file_path: interpret/python/interpret-core/interpret/blackbox/_partialdependence.py
imports: from ..api.base import ExplainerMixin, ExplanationMixin
  import numpy
  from ..utils._explanation import gen_name_from_class, gen_global_selector
  from ..utils._clean_x import preclean_X
  from ..utils._unify_predict import determine_classes, unify_predict_fn
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 2 for calling the PartialDependence obj's underlying member method code with example usage: obj.explain_global(name) and returns: PDPExplanation